How do we develop it?

Typically we start by taking a huge sample of actual sales (or demand) records, correcting it for shortage and any 'funnies'.
5 million or more records is relatively straightforward with modern tools.
The ways we correct for shortage are partly proprietary.
We use 4 methods in concert; all are easier to describe by talking through an example.

"That really worries me. What if it's wrong?How do we cope with 'The Saturday Effect'?"

You are not alone. This worries you and many others … and it shouldn't. Here's just one reason why …
Once tuned, the same stock will meet or beat the service level target on sales from 7 to 27 a period.
If we're a little bit wrong we can tune - or just plain fudge - the critical few, and leave all the rest alone.
And being a little bit wrong some of the time beats the hell out of being a lot wrong all the time!

How do we use it?

If you want to change a consequence, change an input.
It's as easy as that - just like driving a car, really. On short cycle goods we'd typically change Lead Time, which has the biggest impact.
On long cycle goods we'll sometimes need to look elsewhere.
This might include whether those order spikes represent real demand, what service level we can afford, and so on.
As a generality we get the biggest 'quick win' on short cycle goods, and the most varied and unexpected outcomes on long lead time items.
This is because we are forced to look at options we overlooked before.

The reference site has achieved nearly 50% inventory reduction (on $1.9m),
while emergency shipments (as their rough and ready measure of service level) went down by 2/3rds.
With more to come.

Other uses have included:-

Predicting how many more emergency shipments will be needed at fixed Lead
Time and (say) 20% less Stock. Or, at new Stock ceiling, by how much we must reduce Lead Time to restore the Service Level.

Showing how reducing Lead Time by a day will affect Service Level, or Stock, or both.

Quantifying the Service Level and Stock impact of splitting or combining stock points.

Showing how new products will impact Stockholding.

Quantifying the Service Level impact of extending the range into ever slower
movers, 24 hour opening with the same or different delivery times, and/or the Service Level or Stock impact of cross docking.

Above all, we can now provide the tools for defending necessary stock,
and removing unnecessary stock. Because the trade off is a science,
we can now also gain consensus between sales, accountants and operations.

The tools support both fair forecasts and those with 'unwitting bias'.
Any forecast is derived from a pool of potential 'buys' of which actual demand is merely a sample.
With slow movers (and everything moves slowly towards the bottom of the supply chain) the percentage error is greater.
The error in deriving such forecasts is every bit as great as the damage done by believing them.

Computers are fast, accurate and stupid. Humans are slow, inaccurate and brilliant. Together they are powerful beyond belief.